Fundamental Research Challenges Research Articles

Implementing regional blue economy research and innovation strategies: a case study for the Black Sea

The Black Sea is a vital resource with vast potential to boost the societal value of the Blue Economy for its surrounding countries. Improved knowledge and enhanced infrastructure together with better coordination and alignment of research and innovation efforts are critical for the better management of the deteriorated Black Sea ecosystem to help restore and maintain its resilience and enable sustainable use of natural resources. In 2019, the Black Sea Strategic Research and Innovation Agenda (Black Sea SRIA), was developed by the Black Sea experts, in cooperation with European marine institutes and organizations, with the support of the European Commission. Black Sea SRIA presents the priorities and ecosystem-based management options needed for a healthy, sustainable, and resilient Black Sea. Since 2019, efforts focused on developing concrete actions under the Black Sea SRIA Implementation Plan (IP) to contribute to the national blue economy strategies, better structuring of the relevant funding mechanisms for solution-oriented implementation and the international and regional strategies, supporting the co-funding mechanisms. In this study, we present a transnational process that has led to the development of the IP which utilized a bottom-up, co-design and co-creation-based approach. The SRIA and its IP support better governance of the blue economy principles towards sustainable development and conservation of unique Black Sea ecosystems. The Black Sea SRIA and IP address the fundamental research challenges of the region, promote the blue economy, and also build vital support systems and innovative research infrastructure and capacity together with academia, funding organizations, industry, policy, civil society and local communities. The IP is a long-lasting guide to catalyze new ideas and innovations towards and with the Black Sea community with strong implications for other sea basins.

PSInet: a new global water potential network.

Given the pressing challenges posed by climate change, it is crucial to develop a deeper understanding of the impacts of escalating drought and heat stress on terrestrial ecosystems and the vital services they offer. Soil and plant water potential play a pivotal role in governing the dynamics of water within ecosystems and exert direct control over plant function and mortality risk during periods of ecological stress. However, existing observations of water potential suffer from significant limitations, including their sporadic and discontinuous nature, inconsistent representation of relevant spatio-temporal scales and numerous methodological challenges. These limitations hinder the comprehensive and synthetic research needed to enhance our conceptual understanding and predictive models of plant function and survival under limited moisture availability. In this article, we present PSInet (PSI-for the Greek letter Ψ used to denote water potential), a novel collaborative network of researchers and data, designed to bridge the current critical information gap in water potential data. The primary objectives of PSInet are as follows. (i) Establishing the first openly accessible global database for time series of plant and soil water potential measurements, while providing important linkages with other relevant observation networks. (ii) Fostering an inclusive and diverse collaborative environment for all scientists studying water potential in various stages of their careers. (iii) Standardizing methodologies, processing and interpretation of water potential data through the engagement of a global community of scientists, facilitated by the dissemination of standardized protocols, best practices and early career training opportunities. (iv) Facilitating the use of the PSInet database for synthesizing knowledge and addressing prominent gaps in our understanding of plants' physiological responses to various environmental stressors. The PSInet initiative is integral to meeting the fundamental research challenge of discerning which plant species will thrive and which will be vulnerable in a world undergoing rapid warming and increasing aridification.

Machine learning-based product quality classification of the enterprise producing aluminum flat coil

A fundamental research challenge in organizations is determining product quality by constructing prediction models considering the parameters that affect product quality. By solving this problem, shipment planning can be organized quickly based on product quality status, the line can be stopped when necessary, or when it is expected that the required product quality will not be achieved, such products can be planned in the following productions production plan. When such forecasts are not made, the product quality can be recognized much later in the production process through quality control operations. Re-planning and rework expenditures are incurred as a result of this condition. This paper proposes a data-mining-based methodological framework for predicting the quality of flat coils produced by an aluminum flat coil casting company. In the study, the quality of aluminum flat coil was classified considering the data such as OperatorSidePrint, RollEntranceWaterTemperature, UpRollDiameter, DownRollDiameter, UpSpreyVelocity, DownRollExitWaterTemperature, SteelPlateLineVelocity, SteelPlateOperatorSideBending, DownSpreyBulk that affect the product quality by the use of K-means, CLARANS, BIRCH, Ward's Hierarchical Agglomerative Clustering, Clustering, and Logistic Regression, KNN, Artificial Neural Network, CART (Decision Tree), Random Forest (RF), Gradient Boosting Machines, Feed-Forward Neural Network, Naive Bayes, XGBoost and RF, Gradient Boosting RF and AdaBoost-LSTM ensemble models, and Simple RNN classification techniques. Following the application of algorithms to 23 different semi-finished products, RNN provided the best results in 35% of the semi-finished products, and AdaBoost-LSTM provided the best results in 18% of the semi-finished products.

Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing.

The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. As the number of vehicle users and perceived tasks grows, the design of efficient task assignment schemes becomes crucial. However, existing research solely focuses on task deadlines, neglecting the importance of task duration. Additionally, the majority of privacy protection mechanisms in the current task assignment process emphasize safeguarding user location information but overlook the protection of user-perceived duration. This lack of protection exposes users to potential time-aware inference attacks, enabling attackers to deduce user schedules and device information. To address these issues in opportunistic task assignment for vehicular crowdsensing, this paper presents the minimum number of participants required under the constraint of probability coverage and proposes the User-Based Task Assignment (UBTA) mechanism, which selects the smallest set of participants to minimize the payment cost while measuring the probability of accomplishing perceived tasks by user combinations. To ensure privacy protection during opportunistic task assignment, a privacy protection method based on differential privacy is introduced. This method fuzzifies the sensing duration of vehicle users and calculates the probability of vehicle users completing sensing tasks, thus avoiding the exposure of users' sensitive data while effectively assigning tasks. The efficacy of the proposed algorithm is demonstrated through theoretical analysis and a comprehensive set of simulation experiments.

Prediction of melt pool width and layer height for Laser Directed Energy Deposition enabled by physics-driven temporal convolutional network

Laser Directed Energy Deposition (L-DED) is a momentous metal additive manufacturing technology. Owing to high flexibility characteristic, it has been progressively adopted by high-value manufacturing industries. For the technology, one of the fundamental research challenges is how to accurately predict the melt pool size to ensure high-quality L-DED processes. To tackle the challenge, a novel physics-driven temporal convolutional network (TCN) approach is presented. In this research, the high prediction accuracy for L-DED is achievable via the following innovations: (i) a TCN model is designed as the core of the approach to leveraging the distinctive characteristics of the TCN model to address the temporal nature of the L-DED process during heat accumulation and incremental deposition; (ii) the physical models of the peak temperature, Marangoni effect and liquid jets affecting the melt pool formulization during the L-DED process are specified to strengthen the prediction accuracy of the approach. Experiments for manufacturing thin-walled parts using L-DED were conducted for approach validation and analyses. On average, the mean absolute percentage errors (MAPEs) of predicting the melt pool width and the layer height of a melt pool attained by this approach are 3.421% and 4.643%, respectively. The experiments demonstrate that the approach is competent to support the L-DED process in producing good-quality thin-walled parts.

What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

Chemical Recycling of Polymethacrylates Synthesized by RAFT Polymerization.

Reversing controlled radical polymerization and regenerating the monomer has been a long-standing challenge for fundamental research and practical applications. Herein, we report a highly efficient depolymerization method for various polymethacrylates synthesized by reversible addition-fragmentation chain-transfer (RAFT) polymerization. The depolymerization process, which does not require any catalyst, exhibits near-quantitative conversions of up to 92%. The key aspect of our approach is the utilization of the high end-group fidelity of RAFT polymers to generate chain-end radicals at 120 °C. These radicals trigger a rapid unzipping of the polymethacrylates. The depolymerization product can be utilized to either reconstruct the linear polymer or create an entirely new insoluble gel that can also be subjected to depolymerization. Our depolymerization strategy offers a promising route towards the development of sustainable and efficient recycling methods for complex polymer materials.

Deep Reinforcement Learning Based Computation Offloading and Trajectory Planning for Multi-UAV Cooperative Target Search

Unmanned aerial vehicles (UAVs) are widely used for surveillance and monitoring to complete target search tasks. However, the short battery life and moderate computational capability hinder UAVs to process computation-intensive tasks. The emerging edge computing technologies can alleviate this problem by offloading tasks to the ground edge servers. How to evaluate the search process so as to make optimal offloading decisions and make optimal flying trajectories represent fundamental research challenges. In this paper, we propose to utilize the concept of uncertainty to evaluate the search process, which reflects the reliability of the target search results. Thereafter, we propose a deep reinforcement learning (DRL) technique to jointly make optimal computation offloading decisions and flying orientation choices for multi-UAV cooperative target search. Specifically, we first formulate an uncertainty minimization problem based on the established system model. By introducing a reward function, we prove that the uncertainty minimization problem is equivalent to a reward maximization problem, which is further analyzed by a Markov decision process (MDP). To obtain the optimal task offloading decisions and flying orientation choices, a deep Q-network (DQN) based DRL architecture with a separated Q-network is then proposed. Finally, extensive simulations validate the effectiveness of the proposed techniques, and comprehensive discussions on how different parameters affect the search performance are given.

731 Generation of a laser capture microdissection and RNAseq-based human anagen hair follicle transcriptome atlas

Characterizing the transcriptome of defined hair follicle (HF) compartments and cell populations is a fundamental hair research challenge, which has already been mastered for murine HFs (www.hair-gel.net). Yet, a comprehensive atlas of the human HF transcriptome remains to be generated. As a key step into this direction, we have used laser capture microdissection coupled with RNA sequencing to reveal the gene expression profiles of 8 selected compartments of human anagen VI HFs. Principal component analysis (PCA) identified a high degree of similarity of the distinct transcriptional profile of each examined compartment between biological replicates and a clear separation of the HF compartments.

Managing innovation ecosystems around Big Science Organizations

BSOs are large research organizations established purposefully to address fundamental and complex scientific research challenges that cannot be addressed in isolation by individual universities, research institutes, or even government agencies. Unlike universities and other national research institutes, BSOs are unique scientific organizations by virtue of their sheer size, level of complexity, and uncertainty with respect to the outcomes of research and development. BSOs involve large networks of suppliers and collaborators in science, government, and business, constituting a complex system with permeable boundaries that offer opportunities for technology transfer, knowledge accumulation, and business creation. Hence, BSOs are influential players within complex systems of innovation, learning, and business creation. Despite their important role for national and international economies as well as society at large, our current understanding of their management and impact is underdeveloped in both theory and practice. We know less about the challenges and opportunities for innovation and entrepreneurship in a context of changing economic, technological, and societal environments that arise in the broader ecosystem surrounding BSOs.To address this void of research, we made this special issue to focus on innovation and entrepreneurship around BSOs to create a richer foundation for future conceptual and empirical research on science management and innovation. The work included in this special issue offers some new insights regarding innovation and entrepreneurship in the context of BSOs. To embed these individual findings into existing research, we provide a comprehensive overview regarding innovation involving BSOs capturing the full picture of the fundamental issues in this regard. Thus, this introduction of the special issue offers an overview on the innovation ecosystem around BSOs as a common reference point for the fundamental mechanisms of innovation in relation to BSOs and relevant stakeholders.

Deep autoencoder based domain adaptation for transfer learning.

The concept of transfer learning has received a great deal of concern and interest throughout the last decade. Selecting an ideal representational framework for instances of various domains to minimize the divergence among source and target domains is a fundamental research challenge in representative transfer learning. The domain adaptation approach is designed to learn more robust or higher-level features, required in transfer learning. This paper presents a novel transfer learning framework that employs a marginal probability-based domain adaptation methodology followed by a deep autoencoder. The proposed frame adapts the source and target domain by plummeting distribution deviation between the features of both domains. Further, we adopt the deep neural network process to transfer learning and suggest a supervised learning algorithm based on encoding and decoding layer architecture. Moreover, we have proposed two different variants of the transfer learning techniques for classification, which are termed as (i) Domain adapted transfer learning with deep autoencoder-1 (D-TLDA-1) using the linear regression and (ii) Domain adapted transfer learning with deep autoencoder-2 (D-TLDA-2) using softmax regression. Simulations have been conducted with two popular real-world datasets: ImageNet datasets for image classification problem and 20_Newsgroups datasets for text classification problem. Experimental findings have established and the resulting improvements in accuracy measure of classification shows the supremacy of the proposed D-TLDA framework over prominent state-of-the-art machine learning and transfer learning approaches.

The Scholarly Knowledge Ecosystem: Challenges and Opportunities for the Field of Information.

The scholarly knowledge ecosystem presents an outstanding exemplar of the challenges of understanding, improving, and governing information ecosystems at scale. This article draws upon significant reports on aspects of the ecosystem to characterize the most important research challenges and promising potential approaches. The focus of this review article is the fundamental scientific research challenges related to developing a better understanding of the scholarly knowledge ecosystem. Across a range of disciplines, we identify reports that are conceived broadly, published recently, and written collectively. We extract the critical research questions, summarize these using quantitative text analysis, and use this quantitative analysis to inform a qualitative synthesis. Three broad themes emerge from this analysis: the need for multi-sectoral cooperation and coordination, for mixed methods analysis at multiple levels, and interdisciplinary collaboration. Further, we draw attention to an emerging consensus that scientific research in this area should by a set of core human values.

Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions

Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things (IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by integrating UAVs into MEC networks during the current novel coronavirus disease (COVID-19) era. UAV mobile edge computing (MEC) shares personal data with external parties (such as edge servers) during intelligent medical analytics. However, this technique raises privacy concerns about patients’ health data. More recently, the concept of federal learning (FL) has been set up to protect mobile user data privacy. Compared to traditional machine learning, federated learning requires a decentralized distribution system to enhance trust for UAVs. Blockchain technology provides a secure and reliable solution for FL settings between multiple untrusted parties with anonymous, immutable, and distributed features. Therefore, blockchain-enabled FL provides both theories and techniques to improve the performance of intelligent UAV edge computing networks from various perspectives. This survey begins by discussing the current state of research on blockchain and FL. Then, compare the leading technologies and limitations. Second, we will discuss how to integrate blockchain and FL into UAV edge computing networks and the associated challenges and solutions. Finally, we discuss the fundamental research challenges and future directions.

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing. DL models have also been intensely used in different tasks of healthcare such as disease diagnostics and treatments. Deep learning techniques have surpassed other machine learning algorithms and proved to be the ultimate tools for many state-of-the-art applications. Despite all that success, classical deep learning has limitations and their models tend to be very confident about their predicted decisions because it does not know when it makes mistake. For the healthcare field, this limitation can have a negative impact on models predictions since almost all decisions regarding patients and diseases are sensitive. Therefore, Bayesian deep learning (BDL) has been developed to overcome these limitations. Unlike classical DL, BDL uses probability distributions for the model parameters, which makes it possible to estimate the whole uncertainties associated with the predicted outputs. In this regard, BDL offers a rigorous framework to quantify all sources of uncertainties in the model. This study reviews popular techniques of using Bayesian deep learning with their benefits and limitations. It also reviewed recent deep learning architecture such as Convolutional Neural Networks and Recurrent Neural Networks. In particular, the applications of Bayesian deep learning in healthcare have been discussed such as its use in medical imaging tasks, clinical signal processing, medical natural language processing, and electronic health records. Furthermore, this paper has covered the deployment of Bayesian deep learning for some of the widespread diseases. This paper has also discussed the fundamental research challenges and highlighted some research gaps in both the Bayesian deep learning and healthcare perspective.

Behavioral model summarisation for other agents under uncertainty

Modelling other agents is one of the fundamental research challenges in multiagent decision making under uncertainty. A subject agent expects to optimise its own decisions by correctly modelling what other agents are going to act when they share a common environment and their actions have a joint impact in the transition of environmental states and their individual rewards. However, a true model of the other agent is often unknown due to limited prior knowledge, which particularly occurs to a competitive agent setting. Consequently, the subject agent needs to consider a large number of potential models (the number is infinite in theory) for the other agent, which turns to be computationally hard in its decision making process. A feasible solution is to select a subset of potential models that are representative of all the models for the other agent. In this article, we investigate such a model selection given some known behavioral stereotypes ascribed to the other agents. We propose a new measurement to diversify the selected models under the constraint of the number of models (K models). We formulate the top-K model selection as a sub-modular function optimisation problem and develop a greedy algorithm to obtain high-quality solutions. We develop the proposed model selection techniques in a well-recognised multiagent decision model, namely interactive dynamic influence diagrams, that represents individual agents’ decision making process with the consideration of other agents. By doing this, we can test the performance of the new model selection techniques over two problem domains and provide experimental results in support. Hence, our research in this article contributes to a new model selection technique for modelling other agents therefore improving decision quality of a subject agent interacting with the other agents. It also inspires a creative usage of sub-modular function optimisation in multiagent decision making.

Dr. Wenjing Lou, Virginia Tech, USA

Dr. Wenjing Lou is currently the W. C. English Endowed Professor of Computer Science at Virginia Tech and a Fellow of the IEEE. Her research interests span many topics in the fields of wireless networks and cybersecurity. On wireless networks, her interests lie in distributed algorithms and lightweight protocols for wireless networks. Her previous works include algorithm and protocol design for ad hoc networks, multihop cooperative communications, opportunistic routing, and network coding. She has recently collaborated with other colleagues on interference cancellation, MIMO networks, and dynamic spectrum sharing, and 5G wireless communications. On cybersecurity, she has worked on many security and privacy topics in networked information systems, including anonymous authentication, encryption-based information access control and sharing, search over encrypted data, and confidential data computation. More recently, she has been working on block-chain and decentralized consensus protocols, securing 5G/nextG mobile networks, and adversarial machine learning and trustworthy AI systems. Her research goal is to develop innovative solutions to address fundamental research challenges in emerging technologies and critical infrastructures.

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with UQ.

Bioactive anti-inflammatory, antibacterial, conductive multifunctional scaffold based on MXene@CeO2 nanocomposites for infection-impaired skin multimodal therapy

Overcoming multidrug-resistant (MDR) bacterial infection and simultaneously enhancing wound healing/skin reconstruction are still critical challenges for both clinic practice and fundamental research. The single modal therapy strategy is usually inefficient. Herein, for the first time, multifunctional MXene@CeO2 nanocomposites were prepared by combining the 2D antibacterial conductive Ti3C2Tx MXenes and antioxidant CeO2 and applied in developing multifunctional hydrogel scaffold (FOM) for MDR infection-impaired skin multimodal therapy. FOM scaffold was fabricated by incorporating MXene@CeO2 nanocomposites in a dynamic Schiff-based chemical crosslinked hydrogel of polyethylenimine grafted Pluronic F127 (F127-PEI) and oxidized sodium alginate (OSA). FOM scaffold possessed multifunctional properties including injectable self-healing behavior, efficient anti-inflammatory, antibacterial, and antioxidative abilities, conductive bioactivities, tissue-adhesive ability and fast hemostatic capacity. FOM scaffold could promote fibroblasts migration and cell proliferation with electrical stimulation. Additionally, FOM scaffold demonstrated the significant anti-inflammatory and multidrug resistant infection therapy, meanwhile promoting fibroblasts proliferation, granulation tissue formation, collagen deposition, re-epithelialization to accelerate MDR-infected wound healing. This work firstly demonstrated the important role of multifunctional MXene@CeO2 nanocomposites in infected-wound healing/skin reconstruction. This study provided an efficient multimodal therapy on MDR infection-impaired skin via the optimization of the structure and multifunctional properties of biomaterials.

Incentive Mechanism Design for Federated Learning: Challenges and Opportunities

Federated learning is a new distributed machine learning paradigm that many clients (e.g., mobile devices or organizations) collaboratively train a model under the orchestration of a parameter server (e.g., service provider), while keeping the training data locally. One of the main challenges in federated learning is the data island, that is, each client maintains its local data and has no incentive for contributing data to model training if no reward is granted. Thus, we must motivate a large number of clients to participate in federated learning to break the limitation of data in the form of isolated islands. We discuss the fundamental research challenges in the incentive mechanism design for federated learning, and present a general framework with potential solutions to the challenges. Experiments are conducted to verify the effectiveness of the proposed framework. With several future research directions identified in incentive mechanism design for federated learning, we expect that more research interest will be stimulated in this novel area.

Correction to: Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks

Recent advancement in wireless charging technologies has enabled us to design and development of Wireless Rechargeable Sensor Networks (WRSNs) for sensing and data gathering tasks for a very long duration. The fundamental research challenge in WRSN is to design efficient path scheduling for Mobile Wireless Charging Vehicles (MWCVs) such that it maximizes utility of energy resource of MWCVs and minimizes average delay in charging process of the network. Most of the existing solutions for path scheduling of MWCVs suffer from high charging latency,poor energy usage efficiency, and low scalability issues. In order to overcome these issues, this research paper proposed a novel algorithm for scheduling of multiple mobile rechargers using Hybrid meta-heuristic technique. In the proposed Hybrid meta-heuristic-based algorithm, best features of Cuckoo Search and Genetic Algorithm are combined to optimize the path scheduling problem. This work derives a novel fitness function for optimizing the performance of the scheduling. To show the effectiveness of the proposed scheme, an extensive simulation experiments are performed under different network scenarios and results are compared with the latest state-of-art schemes. Result analysis confirms advantages of the proposed scheme in terms of charging latency, total travel distance and energy usage efficiency.